Of all the processes inherent to insurance, none produces such a voluminous amount of disparate data as claims. Insurance Networking News asked Stuart Rose, global insurance marketing manager at Cary, N.C.-based SAS, how claims analytics can be used to unlock efficiencies in the process. INN: Why apply analytics to claims?

SR: Claims analytics is the ability to analyze claims data at each stage in the claims cycle, from entry of first notice of loss through to payout, to make the right decision at the right time to the right party. Rather than analyzing one case at a time-based only on the current information at hand-analytics gives insurers added perspective by allowing them to view claims "in context" by comparing them with previous claims settlements in their database.

INN: How can analytics improve the claims process?

SR: Predictive analytics can enhance the claims process in multiple areas such as optimizing claims settlements, improving loss reserving, preventing fraud, discovering unforeseen subrogation opportunities and better resource allocation that ultimately leads to increased customer satisfaction.

One of the challenges insurers face today is the inability to accurately forecast the loss reserve and, ultimately, predict the outcome once the claim has been submitted. By applying time series and econometric analysis, it is possible to calculate an accurate loss reserve amount and benchmark each claim based on similar characteristics and, hence, reduce the propensity for claims padding.

By implanting data mining techniques to cluster and group loss characteristics, claims can be scored, prioritized and assigned to the most appropriate adjuster.

Today, most insurers have implemented fast-track settlement and mobile claims processes that settle claims instantly. But writing a check on the spot can be costly if the insurer overpays. By analyzing claims and claim histories, companies can present realistic limits for instant payouts that will satisfy the customer and ensure the insurance company remains profitable.

Finally, claims analytics is proving successful in reviewing claims for possible litigation. Analytics can be used to predict claims that may result in litigation, prioritize resources and mitigate the severity of the claim.

INN: Will a carrier have to modify its existing process to employ analytics?

SR: No, the beauty of claims analytics is that it works in conjunction with an insurer's existing claims process. The strength of claims analytics lies in the amount and quality of available data. Whenever data is entered or updated for the claims, analytics can be used to reassess the loss reserve, or as in the case of a workers' compensation bodily injury claims, recalculate a return-to-work date.

A consequence of using claims analytics is that it may highlight operational inefficiencies in an insurance carrier's claims cycle, resulting in possible changes to the claims process. Analytics has shown that the size of a settlement payout increases in relation to the number of days between when the claims occurred and when it was reported. For example, a claim that is not reported within the first four days will, on average, be 50% greater than a claim reported immediately.

Analytics is a developing process, and is one that is never "done." Over time, an insurer will collect more data to analyze gaining greater insight into its claims process.

INN: Does analytics only apply to numerical data?

SR: Another major problem facing insurance companies today, often a limitation with legacy claims systems, is that a large amount of the information relating to claims is considered unstructured data, i.e. adjuster notes, medical records and police reports.

Using text mining capabilities helps insurers to analyze this invaluable data, improving the benefits that can be achieved from implementing claims analytics. For example, an adjuster will not be aware that his assigned case involves a medical specialist who bills for a large number of treatments as compared to another medical specialist billing for similar injuries. But if the insurer can analyze the text embedded in medical claims, the possibility of fraud or claims padding becomes evident, and can be the basis of further investigation.

INN: What about ROI?

SR: The return on investment for claims analytics is extremely compelling, especially with the ability to dramatically reduce insurers claims expenses and exposure to fraud.

As insurance becomes a commodity, insurers need to consider how they can differentiate themselves from their competitors. Analytics can deliver the competitive advantage in measurable ROI with cost savings and increased profits.

(c) 2008 Insurance Networking News and SourceMedia, Inc. All Rights Reserved.

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